An unmanned surface vehicle course stringer control method and system

Abstract

This invention relates to a cascaded heading control method and system for unmanned surface vessels (USVs). The method includes the following steps: constructing an inner-loop heading control system based on an FOPID controller, used to output a control quantity to drive the USV's steering system based on the deviation between the USV's desired heading and its actual heading; constructing an outer-loop heading control system based on an ADRC controller, used to output the USV's desired heading based on the deviation between the USV's desired heading and its actual heading, as well as the USV's current total disturbance; using the USV's desired heading output from the outer-loop heading control system as the input to the inner-loop heading control system to form a cascaded control system; inputting a step signal into the cascaded control system to simulate the USV's desired heading, thereby controlling the USV's heading through the cascaded control system. This invention effectively improves the heading maintenance and tracking capabilities of high-speed USVs in complex sea conditions, enhancing the reliability and safety of USV surface operations.

Description

Technical Field

[0001] This invention belongs to the field of unmanned surface vessel (USV) heading control technology, and particularly relates to a USV heading cascade control method and system. Background Technology

[0002] An unmanned surface vessel (USV) is a vessel that uses automatic control principles to achieve autonomous navigation or remote control, enabling it to complete tasks without or with minimal human intervention. Electro-hydraulic servo steering is one of the main steering drive methods for high-speed USVs, used to achieve power drive and rudder angle control for rudder steering.

[0003] However, while existing electro-hydraulic servo steering has advantages such as sufficient power and low power consumption, its hydraulic transmission has nonlinearity and inertial time delay, which can easily affect the heading control of unmanned surface vessels.

[0004] In addition, when unmanned surface vessels (USVs) are traveling at high speeds, they are easily affected by the highly time-varying complex marine environment, such as wind, waves, and currents, making it difficult for USVs to maintain their course. Summary of the Invention

[0005] The purpose of this invention is to solve one of the above-mentioned technical problems and to provide a method and system for cascade control of the heading of an unmanned surface vessel.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A cascaded heading control method for an unmanned surface vessel, characterized by comprising the following steps: An inner-loop steering control system for an unmanned surface vessel (USV) is constructed. The inner-loop steering control system employs a fractional-order proportional-integral-derivative (FOPID) controller, which outputs control quantities to drive the USV's steering system based on the deviation between the USV's desired steering direction and its actual steering direction. An outer-loop heading control system for the unmanned surface vessel (USV) is constructed. The outer-loop heading control system adopts an ADRC controller, which is used to output the desired rudder direction of the USV based on the deviation between the desired and actual headings and the current total disturbance of the USV. The desired rudder direction of the unmanned surface vessel output from the outer ring heading control system is used as the input to the inner ring rudder control system to form a cascade control system. A step signal is input into the cascade control system to simulate the desired course of the unmanned surface vessel (USV), so that the USV can be controlled by the cascade control system.

[0007] In some embodiments of the present invention, the inner-loop steering control system includes an FOPID controller and an electro-hydraulic servo steering model; The FOPID controller is used to output control quantities for the electro-hydraulic servo steering model based on the deviation between the desired and actual steering direction of the unmanned surface vessel. The electro-hydraulic servo steering model is used to control the steering of the unmanned surface vessel's rudder based on the control quantity driven rudder steering system, and output the actual rudder direction of the unmanned surface vessel. In some embodiments of the present invention, the outer-loop heading control system includes an ADRC controller and an unmanned surface vessel maneuvering response model; The ADRC controller is used to output the desired rudder direction of the unmanned surface vessel (USV) based on the deviation between its desired and actual heading. The unmanned surface vessel (USV) maneuver response model is used to output the actual heading of the USV based on its actual steering direction and the total disturbance during its movement. In some embodiments of the present invention, the expression for the unmanned surface vessel (USV) maneuvering response model is as follows: ; in, , , It is a time constant. For steering capability index, The total interference when the unmanned surface vessel is in motion. Yaw rate, Yaw angle For rudder angle.

[0008] In some embodiments of the present invention, the method for constructing an inner-loop steering control system for an unmanned surface vessel specifically includes the following steps: Using a natural heuristic optimization algorithm, the parameters of the FOPID controller are optimized and tuned with a preset comprehensive performance index as the optimization target until the step response of the inner loop steering control system meets the preset performance requirements, and the optimized FOPID controller parameters are fixed. After the parameters of the FOPID controller are fixed, a natural heuristic optimization algorithm is used to optimize and tune the parameters of the ADRC controller with the preset comprehensive performance index as the optimization target until the step response of the outer loop heading control system meets the preset performance requirements, and the optimized ADRC controller parameters are fixed.

[0009] In some embodiments of the present invention, the natural heuristic optimization algorithm is any one of the following: Hybrid Mean Center Reverse Learning Particle Swarm Optimization (HCOPSO), Particle Swarm Optimization, Ant Colony Optimization, Genetic Algorithm, Simulated Annealing, Gray Wolf Optimization, or Whale Optimization.

[0010] In some embodiments of the present invention, the comprehensive performance index is composed of a weighted combination of a time-weighted absolute error integral index, an overshoot penalty function, and a settling time penalty function; the expression for the comprehensive performance index is: ; in, For comprehensive performance indicators, The time-weighted absolute error integral index. and All are weighting factors. This is the overshoot penalty function. To adjust the time penalty function; The expression for the absolute error of the time-weighted integral is: ; in, This represents the error between the actual output and the expected output. For time; The expression for the overshoot penalty function is: ; in, For overshoot, For the maximum overshoot, This is the actual output, and , , .

[0011] The expression for adjusting the time penalty function is: ; in, To adjust the time, Given the desired adjustment time.

[0012] In some embodiments of the present invention, when the natural heuristic optimization algorithm is the HCOPSO algorithm, the method for optimization tuning specifically includes the following steps: The set of controller parameters to be optimized is defined as the position vector of the particle in the search space; within the preset parameter value boundary, a population consisting of multiple particles is randomly initialized, and each particle is randomly assigned an initial velocity vector; The position vector of each particle is decoded into a set of specific controller parameters and substituted into the model; the preset comprehensive performance index is used as the fitness function to perform simulation evaluation on each particle; the current position of each particle is recorded as its individual historical best position, and the position of the particle with the best fitness is selected from all particles and recorded as the current global best position. Based on the individual historical best position and the global best position, during the iteration process, the velocity vector and position vector of each particle in the population are updated according to the velocity and position update formula of the standard particle swarm optimization algorithm. The mean center and partial mean center are calculated based on a predetermined calculation formula, and then the mixed mean center is calculated based on the mean center and partial mean center. By performing reverse learning on the mixed mean center, the reverse solution of the mixed mean center is generated; Compare the fitness of the reverse solution with the current global optimum. If the reverse solution is better, replace the current global optimum with the reverse solution. The current global optimal position is decoded into a set of controller parameters and substituted into the cascade control system; the simulation is run to calculate the response data of the cascade control system under a step signal input, and the comprehensive performance index is calculated based on the response data as the optimization target value for the current iteration. When the preset algorithm termination condition is met, the iteration terminates, and the optimal controller parameter set obtained by decoding the final global optimal position is output as the optimization result.

[0013] Some embodiments of the present invention further provide an unmanned surface vessel (USV) heading cascade control system for implementing the above-described USV heading cascade control method, comprising: The inner loop steering control module includes an FOPID controller and an electro-hydraulic servo steering model. The FOPID controller is used to output a control quantity for the electro-hydraulic servo steering model based on the deviation between the desired steering direction and the actual steering direction of the unmanned surface vessel (USV). The electro-hydraulic servo steering model is used to drive the USV's steering system based on the control quantity to control the USV's steering direction and output the USV's actual steering direction. The outer-loop heading control module includes an ADRC controller and an unmanned surface vessel (USV) maneuvering response model. The ADRC controller outputs the USV's desired rudder direction based on the deviation between the USV's desired and actual heading, which serves as the input to the inner-loop rudder direction control module. The USV maneuvering response model outputs the USV's actual heading based on the USV's actual rudder direction and the total disturbance during USV operation.

[0014] In some embodiments of the present invention, the invention further includes: The parameter tuning module is used to perform parameter optimization tuning operations. The parameter tuning module includes a first optimization unit and a second optimization unit. The first optimization unit is used to optimize the parameters of the FOPID controller using a natural heuristic optimization algorithm with a preset comprehensive performance index as the target, and fix the optimization result. The second optimization unit is used to optimize the parameters of the ADRC controller by adopting a natural heuristic optimization algorithm after fixing the parameters of the FOPID controller, with the preset comprehensive performance index as the target, and then fix the optimization results.

[0015] The beneficial effects of this invention are as follows: 1. This invention achieves complementary advantages between the outer and inner loops by constructing a cascade control architecture of an outer loop ADRC controller and an inner loop FOPID controller. The outer loop ADRC controller focuses on estimating and compensating for comprehensive environmental disturbances such as wind, waves, and currents, effectively improving the anti-interference capability of heading control. The inner loop FOPID controller handles the nonlinearity and inertial delay of the electro-hydraulic servo steering system, improving the dynamic response speed and steady-state accuracy of rudder tracking. Compared with traditional PID or single controller solutions, it has a shorter settling time, smaller overshoot, and better steady-state accuracy. 2. This invention employs a natural heuristic optimization algorithm for the outer-loop ADRC controller and the inner-loop FOPID controller, using an optimization order of inner loop first and then outer loop. When the HCOPSO algorithm is preferred, it can improve the problem of premature convergence of the particle swarm optimization algorithm and efficiently obtain the globally approximately optimal combination of controller parameters. 3. This invention combines the time-weighted absolute error integral index with the step response index characterizing dynamic characteristics through a penalty function to construct a comprehensive performance index as the optimization objective function. This allows the parameter optimization process to not only focus on steady-state error, but also force the optimization algorithm to constrain and optimize key dynamic performances such as overshoot and adjustment speed of the system, thus achieving the best balance between system response speed, stability and accuracy.

[0016] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of a cascaded heading control method for an unmanned surface vessel; Figure 2 An architecture diagram of a cascade control system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the FOPID controller provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the NADRC controller provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of the LADRC controller provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the overall structure of the cascade control system provided in an embodiment of the present invention; Figure 7 A flowchart for optimizing controller parameters using the HCOPSO algorithm provided in an embodiment of the present invention; Figure 8 A schematic diagram of the output response curves of the FOPID controller and the PID-based inner loop steering controller provided in an embodiment of the present invention; Figure 9 A schematic diagram comparing the anti-interference performance of the FOPID controller and the PID-based inner loop steering controller provided in this embodiment of the invention. Figure 10 This is a schematic diagram of the heading control output response curves of the "LADRC-FOPID" cascade control system and the "PID-FOPID" cascade control system. Figure 11 This diagram illustrates a comparison of the heading control responses of the "LADRC-FOPID" cascade control system and the "PID-FOPID" cascade control system under complex sea conditions. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0020] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations according to this application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. Furthermore, it should be understood that the terms “comprising” and “having” and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] To better explain the solution of the present invention, the background technology of the present invention will be described first.

[0022] In the field of unmanned surface vessel (USV) heading control, high-precision control and excellent maneuverability are required for USVs to operate safely in water environments. The motion control system of a USV is typically divided into upper-level path-tracking control and lower-level heading control. The lower-level heading control ensures the USV has good heading maneuverability.

[0023] High-speed unmanned surface vessels (USVs) represent one of the future development directions for USVs, and high-speed travel places higher demands on the heading and maneuvering performance of USVs.

[0024] Electro-hydraulic servo steering system (EHSSS) is one of the main steering drive methods for high-speed unmanned surface vessels (USVs), used to achieve power drive and angle control for rudder steering. While EHSSS offers advantages such as sufficient power and low power consumption, its hydraulic transmission is affected by nonlinearity and inertial time delay, requiring a high-performance rudder control method to match it. Furthermore, high-speed USVs travel at high speeds and are easily affected by wind, waves, and currents, making course maintenance more challenging and rudder control susceptible to highly time-varying external disturbances.

[0025] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0026] The technical solution of the present invention will be described in detail below with reference to specific embodiments and accompanying drawings.

[0027] As attached Figure 1 - Appendix Figure 11 As shown, in an illustrative embodiment of an unmanned surface vessel heading cascade control method of the present invention, the cascade control method includes the following steps.

[0028] S1: Construct the inner-loop steering control system for the unmanned surface vessel (USV). The inner-loop steering control system employs a FOPID (Fractional Order Proportional-Integral-Derivative) controller, which operates a control algorithm based on the deviation between the USV's desired steering direction and its actual steering direction. The output control quantity drives the electro-hydraulic servo steering model, thereby driving the USV's steering system.

[0029] S2: Construct an outer-loop heading control system for the unmanned surface vessel (USV) to address the heading control problem of high-speed USVs affected by wind, waves, and current interference. The outer-loop heading control system employs an ADRC (Active Disturbance Rejection Control) controller to output the desired rudder direction of the USV based on the deviation between the desired and actual heading of the USV, as well as the current total disturbance of the USV.

[0030] In some embodiments of the present invention, the outer-loop heading control system includes an ADRC controller and an unmanned boat maneuvering response model.

[0031] The ADRC controller is used to output the desired rudder angle of the unmanned boat based on the deviation between the desired heading and the actual heading of the unmanned boat.

[0032] The unmanned boat maneuvering response model is used to output the actual heading of the unmanned boat based on the actual rudder angle of the unmanned boat and the total disturbance during the travel of the unmanned boat.

[0033] Among them, ADRC is a control method that does not rely on an accurate mathematical model and can effectively cope with nonlinearity, uncertainty, and external disturbances. This method is an improvement based on the error feedback idea of traditional PID control, retaining the advantages of simple structure and easy implementation of PID, while overcoming its limitations in error extraction and weighting processing. The core concept of this method is to uniformly regard the unknown internal dynamics and external disturbances of the system as "total disturbances", and use an Extended State Observer (ESO) to estimate and compensate them in real time, so as to actively suppress disturbances and achieve precise control and excellent dynamic performance of the system.

[0034] In the embodiments of the present invention, the outer-loop heading control system adopts the LADRC controller in the ADRC controller as a specific implementation manner.

[0035] The ADRC controller is divided into a Nonlinear ADRC (NADRC) controller and a Linear ADRC (LADRC) controller. The system structure of the NADRC controller is as Figure 4 shown, which consists of three parts: a tracking differentiator, an ESO, and a nonlinear state error feedback. The high-speed USV heading control provided by the embodiments of the present invention is a single-input single-output system, and its mathematical form is: (1).

[0036] Among them, is the actual heading, is the heading angular velocity, is the control input, which is expressed as the desired rudder angle in Figure 2 , is the control input gain, is the external disturbance, including the external interference during the travel of the high-speed USV and the interference received by the electro-hydraulic servo rudder steering model.

[0037] Let the state variables 、 , then the state space equation of Equation (1) can be expressed as: (2).

[0038] in, This is the system output. To more effectively respond to the effects of disturbances, [the following will be implemented / implemented]: Treat it as the total disturbance and expand it into a new state variable of the system, that is, let Thus, the expanded system is obtained: (3).

[0039] Based on this, the NADRC controller algorithm consists of three parts: ESO, TD, and NLSEF. The expression for the ESO algorithm is as follows: (4).

[0040] in, , , These are the state variables of ESO. It is the sampling period. , , These are the parameters for ESO.

[0041] In equation (4), the estimated value is used. To approximate the control input gain ,Will The unknown parts are also treated as part of the disturbance.

[0042] It is a nonlinear function: (5).

[0043] The expression for the TD algorithm is: (6).

[0044] in, Defined as the fastest synthesis function: (7).

[0045] In equation (6), and These are the parameters of TD. Determined by the speed of the tracking process. Sampling period Integer multiples of. TD can achieve Quickly and without overshoot, follow the input signal , its in Figure 2 The middle represents the input heading. As The approximate derivative is used to track the differential signal of the process.

[0046] The expression for the NLSEF algorithm is: (8).

[0047] in, To control the gain, The damping factor, This is the precision factor.

[0048] The final control quantity, i.e. the desired rudder direction, is expressed as: (9).

[0049] The structure of the LADRC controller is as follows: Figure 5 As shown, the LADRC controller is a simplified improvement based on the NADRC controller, which linearizes ESO and NLSEF.

[0050] The mathematical form of a LADRC controller can be expressed as: (10).

[0051] in, Given a value, i.e., the desired heading; For the observer bandwidth, This is the controller bandwidth. The final control quantity remains: .

[0052] The adjustment parameters for the LADRC controller are: control input gain estimate. Observer bandwidth Controller bandwidth .

[0053] In some embodiments of the present invention, the unmanned surface vessel (USV) maneuvering response model is driven by an electro-hydraulic servo steering model, which uses rudder steering... As input, the unmanned surface vessel's heading As output, the expression for the unmanned surface vessel (USV) maneuver response model is: (11).

[0054] in, , , It is a time constant. For steering capability index, The total disturbance during the operation of the unmanned surface vessel (USV) includes environmental disturbances such as wind, waves, and currents, as well as the inherent uncertainties of the USV itself. Yaw rate, Yaw angle For rudder angle.

[0055] When a high-speed USV navigates on the water surface, as its speed increases, the hydrodynamic force generated by the rudder surface is proportional to the square of the flow velocity, leading to an enhanced rudder effect and faster steering response. Simultaneously, the relative effects of hydrodynamic damping and added mass force decrease, resulting in a smaller system inertial time constant and a faster yaw response. In this embodiment, the operational performance index in the unmanned surface vessel's handling response model is set as follows: .

[0056] In some embodiments of the present invention, the inner-loop steering control system includes an FOPID controller and an electro-hydraulic servo steering model.

[0057] The FOPID controller is used to output control quantities for the electro-hydraulic servo steering model based on the deviation between the desired and actual steering direction of the unmanned surface vessel.

[0058] The electro-hydraulic servo steering model is used to control the steering system of unmanned surface vessels (USVs) based on control variables, and outputs the actual steering direction of the USV.

[0059] The system architecture of the FOPID controller is as follows: Figure 3 As shown, in the design process of the FOPID controller, compared with the conventional PID, FOPID introduces two new parameters: the derivative order. and integral order The controller parameters have been increased from three parameters in a conventional PID controller to five parameters. This increase in the number of controller parameters allows for a wider adjustable range, a more precise control model, and more flexible and convenient control of the controlled object. This enables the controlled object to achieve better dynamic and static characteristics, thus meeting the various performance indicators of complex systems.

[0060] The transfer function of the FOPID controller is as follows: (12).

[0061] The adjustment parameters of the FOPID controller include: proportional coefficient. Integral coefficient Differential coefficients Integral order Differential order .

[0062] The steering system of the high-speed USV driven by the electro-hydraulic servo steering model mainly consists of a pump-controlled power mechanism and a steering mechanism. The pump-controlled power mechanism mainly includes a servo motor and a bidirectional variable pump, while the steering mechanism mainly includes a hydraulic cylinder plunger and a stern engine connecting rod.

[0063] The process of achieving high-speed USV steering by driving the electro-hydraulic servo steering model with the FOPID controller is mainly divided into two parts: the process of the servo motor driving the bidirectional variable pump in the pump-controlled power mechanism, and the process of the bidirectional variable pump driving the steering mechanism.

[0064] In some embodiments of the present invention, the method for constructing an electro-hydraulic servo steering model specifically includes the following steps: The process of a servo motor driving a bidirectional variable pump in a pump control power mechanism is modeled.

[0065] The controller's output signal, after being transformed by the H-bridge drive circuit, controls the speed and direction of the servo motor. The servo motor and the bidirectional variable pump are connected by a fixed mechanical structure, and their speeds and directions can be approximately synchronized. The rotation of the servo motor is a first-order inertial element, and the process of transforming the drive signal can be considered as a gain. The mathematical form of this process can be expressed as: (13).

[0066] in, The command signal output by the controller. This refers to the rotational speed of the bidirectional variable pump; The electromechanical time constant. The rate of change of rotational speed. Theoretically, it is not subject to amplitude constraints and can respond continuously and smoothly to changes in the input signal.

[0067] The process of a bidirectional variable pump driving a steering mechanism is modeled.

[0068] The rotation of the bidirectional variable pump drives the flow of hydraulic fluid, which in turn drives the piston in the steering mechanism to perform linear reciprocating motion. Through a mechanical transmission mechanism between the piston and the stern engine connecting rod, the linear displacement of the piston is converted into the deflection motion of the stern engine connecting rod. The deflection angle is the steering direction (rudder angle) of the electro-hydraulic servo steering model.

[0069] For the oil flow caused by the rotation of the bidirectional variable pump, internal oil leakage should be considered. Oil compression Fixed volume: (14).

[0070] The flow rate provided by the bidirectional variable pump is , , And obtained from Hooke's Law , This represents the linear displacement of the cylinder piston. This refers to the internal hydraulic pressure of the oil cylinder. For flow coefficient, For static leakage coefficient, The dynamic compression factor. The effective area of ​​the hydraulic cylinder. is the Hooke coefficient.

[0071] The linear displacement of the hydraulic cylinder piston is converted into the deflection motion of the stern engine connecting rod, which is approximately a linear conversion. (15).

[0072] in, For geometric coefficients. Combining equations (14) and (15), the mathematical expression for the process of the bidirectional variable pump driving the steering mechanism is obtained as follows: (16).

[0073] in, Let be the time constant of the process. The gain of this process, For the rudder angle, certain conditions must be met: , .

[0074] In this embodiment of the invention, a gain stage is provided. Electromechanical time constant The time constant of the bidirectional variable pump driving the steering mechanism Gain .

[0075] S3: Using the desired rudder direction of the unmanned surface vessel output from the outer-loop heading control system as the input to the inner-loop rudder direction control model, a cascade control system based on ADRC and FOPID is built. The structure of the cascade control system is as follows: Figure 2 and Figure 6 As shown.

[0076] In some embodiments of the present invention, the input of the cascade control system is selected as a step signal to simulate the desired heading of the high-speed unmanned surface vessel (USV). The inner loop of the cascade control system uses an FOPID controller to drive the electro-hydraulic servo steering system to achieve stable steering of the high-speed USV. The outer loop of the cascade control system uses a LADRC controller as a specific implementation of the ADRC controller. The deviation between the desired heading signal and the actual heading signal fed back by the system is calculated by the LADRC controller to determine the rudder control quantity. The deviation between the rudder control quantity calculated by the LADRC controller and the actual rudder direction fed back by the inner loop system is calculated by the FOPID controller to determine the control quantity, thereby controlling the electro-hydraulic servo steering model, and ultimately controlling the steering system of the USV to achieve accurate heading tracking. During this process, the LADRC controller and the FOPID controller calculate their respective control quantities according to their preset formulas.

[0077] S4: Input a step signal into the cascade control system to simulate the desired course of the unmanned surface vessel (USV), so as to control the course of the USV through the cascade control system.

[0078] In some embodiments of the present invention, the following steps are further included: In step S1, a natural heuristic optimization algorithm is used to optimize and tune the parameters of the FOPID controller with the preset comprehensive performance index as the optimization target until the step response of the inner loop steering control system meets the preset performance requirements, and the optimized FOPID controller parameters are fixed. After the parameters of the FOPID controller are fixed in step S2, the parameters of the ADRC controller are optimized and tuned using a natural heuristic optimization algorithm with the preset comprehensive performance index as the optimization target until the step response of the outer loop heading control system meets the preset performance requirements, and the optimized ADRC controller parameters are fixed.

[0079] In some embodiments of the present invention, steps S1 and S2 specifically include the following steps: An inner-loop steering control system is constructed, with the inner-loop control loop as an independent system and the input signal set as a step signal.

[0080] The parameters of the inner-loop FOPID controller are optimized using a natural heuristic optimization algorithm to ensure that the step response of the inner-loop FOPID control system is stable and meets performance requirements.

[0081] Once the inner loop FOPID controller parameters are fixed and the inner loop response performance is determined, the optimized set of FOPID controller parameters will be fixed and will not be changed.

[0082] An outer-loop heading control system is constructed based on the inner-loop FOPID control system to form a cascade control system. With the parameters of the inner-loop FOPID controller fixed, the outer-loop heading control system is built so that the output of the outer-loop ADRC controller is used as the input of the inner-loop rudder control system.

[0083] Optimize the parameters of the outer-loop ADRC controller while keeping the parameters of the inner-loop FOPID controller unchanged. Use a natural heuristic optimization algorithm to optimize the parameters of the outer-loop ADRC controller, so that the step response of the cascade control system is stable and meets the performance requirements.

[0084] Fix the outer loop ADRC controller parameters. Once the outer loop response meets expectations, fix the ADRC controller parameters.

[0085] In some embodiments of the present invention, the natural heuristic optimization algorithm is any one of the following: hybrid mean center backward learning particle swarm optimization algorithm, particle swarm optimization algorithm, ant colony optimization algorithm, genetic algorithm, simulated annealing algorithm, gray wolf optimization algorithm, or whale optimization algorithm.

[0086] In some embodiments of the present invention, the natural heuristic optimization algorithm uses an error integral class metric as a performance indicator. The error integral class metric includes: Integral of Squared Error (ISE): (17).

[0087] Integral of Absolute Error (IAE): (18).

[0088] Integral of Time-weighted Squared Error (ITSE): (19).

[0089] Integral of Time-weighted Absolute Error (ITAE): (20).

[0090] It should be noted that different performance indicators reflect different focuses. Control systems designed according to ISE and IAE have faster response speeds and larger oscillations, but relatively poor stability. ITSE and ITAE focus on the errors that appear in the later stage of the transient response and pay less attention to the large initial errors in the response, so that the system can approach the target transient response with less oscillation in a shorter time. In the existing technology, most of the ITAE indicators are used as performance indicators for parameter optimization.

[0091] Because high-speed USVs have high control requirements, in some embodiments of this invention, the time-weighted absolute error integral is combined with the penalty functions for overshoot and settling time in the step response index to construct a comprehensive performance index for parameter optimization. That is, the comprehensive performance index is composed of a weighted combination of the time-weighted absolute error integral index, the overshoot penalty function, and the settling time penalty function.

[0092] Specifically, the expression for the comprehensive performance index is as follows: (twenty one).

[0093] in, For comprehensive performance indicators, The time-weighted absolute error integral index. and All are weighting factors. This is the overshoot penalty function. This is for adjusting the time penalty function.

[0094] The expression for the time-weighted absolute error integral is: .

[0095] in, This represents the error between the actual output and the expected output. For time.

[0096] The expression for the overshoot penalty function is: (twenty two).

[0097] in, For overshoot, For the maximum overshoot, This is the actual output, and , , .

[0098] The expression for adjusting the time penalty function is: (twenty three).

[0099] in, To adjust the time, Given the desired adjustment time.

[0100] In some embodiments of the present invention, such as Figure 7 As shown, when the natural heuristic optimization algorithm is the HCOPSO algorithm, the optimization tuning method specifically includes the following steps: The set of controller parameters to be optimized is defined as the position vector of the particle in the search space; within the preset parameter value boundary, a population consisting of multiple particles is randomly initialized, and each particle is randomly assigned an initial velocity vector.

[0101] Specifically, let the particle swarm be... , ,and , .in, The number of particles in the swarm. It is a particle dimension. Indicates the first The particle in the first Position on the dimension This represents the corresponding speed. Among them, Let represent the fitness function, as expressed by equation (21).

[0102] Set individual learning factors Social learning factors Inertia factor Maximum number of iterations Number of particle groups And other related parameters.

[0103] The parameters to be optimized in the controller are used as particle swarm optimization. : If the parameters are optimized for the inner-loop FOPID controller, then the proportional coefficient will be adjusted. Integral coefficient Differential coefficients Integral order Differential order As a group of particles and set dimensions It is 5.

[0104] If the parameter optimization is for the outer loop ADRC controller, then the estimated control input gain value will be... Observer bandwidth Controller bandwidth As a group of particles and set dimensions The value is 3.

[0105] Within the search space Internally initialize the position of each particle ,speed .

[0106] The position vector of each particle is decoded into a set of specific controller parameters and substituted into the model; a preset comprehensive performance index is used as the fitness function to simulate and evaluate each particle; the current position of each particle is recorded as its individual historical best position, and the position of the particle with the best fitness is selected from all particles and recorded as the current global best position.

[0107] Specifically, calculate the fitness of each particle. At the same time Assign to To make it a particle Initial individual optimal position The particle with the best fitness is selected as the global optimal vector, i.e. ,in , Indicates the globally optimal particle at the th During the nth iteration The position of the dimension.

[0108] Based on the individual historical best position and the global best position, during the iteration process, the velocity vector and position vector of each particle in the population are updated according to the velocity and position update formula of the standard particle swarm optimization algorithm.

[0109] Specifically, according to the speed formula of the particle swarm optimization algorithm: , And, the position formula: , Update Particle Swarm The position and velocity of each particle.

[0110] Where t is the number of iterations. , Represents a random number in the interval [0,1]. Represents particles Iterate to The historical best position was found at that time.

[0111] The mean center and partial mean center are calculated based on a predetermined calculation formula, and then the mixed mean center is calculated based on the mean center and partial mean center.

[0112] Specifically, calculate the mean center (MC). Based on the formula: , The first step in constructing a particle swarm Mean center .

[0113] However, combining equation (21) and according to the formula: , Calculate the first particle swarm Dimensional average fitness .

[0114] Further based on the formula: , Choose the first In a swarm of particles, particles with a fitness better than the average fitness are considered high-quality particles. For the first The fitness of each particle.

[0115] Calculate the Partial Mean Center (PMC). According to the formula: , The first step in constructing a population The partial mean center of the position of the dimensional particle .

[0116] in, For the first Weizhongdi The position of a high-quality particle, and , The number of high-quality particles.

[0117] Calculate the Hybrid Mean Center (HMC). Using the formula: , By combining the mean center with the partial mean center, a more advantageous mixed mean center can be constructed. Participate in iterative optimization.

[0118] The mixed mean center is learned by backpropagation to generate a backward solution. The fitness of the backward solution is compared with that of the current global optimum. If the backward solution is better, the current global optimum is replaced by the backward solution.

[0119] Specifically, according to the formula: , Back-learning the mixed mean center to generate the inverse solution Participate in iterative optimization.

[0120] in, It is a random number on (0,1). and They are In the The minimum and maximum values ​​at the nth iteration, i.e. , .

[0121] like Then use Replace the current global optimal position Otherwise, the current global optimal position It remains unchanged.

[0122] The current global optimal position is decoded into a set of controller parameters and substituted into the cascade control system. Simulation is run to calculate the response data of the cascade control system under a step signal input, and the comprehensive performance index is calculated based on the response data as the optimization target value for the current iteration.

[0123] Specifically, from the current global optimal position Generate a set of controller parameters: if it is an FOPID controller, then... , , , , If it is an ADRC controller, then it is , , .

[0124] The controller parameters are output to the inner loop rudder control system or the outer loop heading control system, so that the system makes a deviation between the step input signal and the rudder angle value output by the inner loop control system or the heading angle value output by the outer loop control system, and performs FOPID control algorithm or ADRC control algorithm calculation according to the sampling period, and further calculates the performance index shown in formula (21).

[0125] Determine whether the termination condition is met. Determine whether the termination condition is met based on the performance index shown in equation (21) or the number of iterations.

[0126] When the preset algorithm termination condition is met, the iteration terminates, and the optimal controller parameter set obtained by decoding the final global optimal position is output as the optimization result.

[0127] Some embodiments of the present invention further include an unmanned surface vessel (USV) heading cascade control system for implementing the above-described USV heading cascade control method, comprising an inner loop steering control module and an outer loop heading control module.

[0128] The inner-loop steering control module includes an FOPID controller and an electro-hydraulic servo steering model. The FOPID controller is used to output a control quantity for the electro-hydraulic servo steering model based on the deviation between the desired steering direction and the actual steering direction of the unmanned surface vessel (USV). The electro-hydraulic servo steering model is used to drive the USV's steering system based on the control quantity to control the USV's steering and output the USV's actual steering direction.

[0129] The outer loop heading control module includes an ADRC controller and an unmanned surface vessel (USV) maneuvering response model. The ADRC controller outputs the desired rudder direction of the USV based on the deviation between its desired and actual heading. The USV maneuvering response model outputs the actual heading of the USV based on its actual rudder direction and the total disturbance during its movement, which serves as the input to the inner loop heading control model.

[0130] In some embodiments of the present invention, a parameter tuning module is further included.

[0131] The parameter tuning module is used to perform parameter optimization tuning operations. The parameter tuning module includes a first optimization unit and a second optimization unit.

[0132] The first optimization unit is used to optimize the parameters of the FOPID controller using a natural heuristic optimization algorithm with a preset comprehensive performance index as the target, and then fix the optimization result.

[0133] The second optimization unit is used to optimize the parameters of the ADRC controller by adopting a natural heuristic optimization algorithm after fixing the parameters of the FOPID controller, with the preset comprehensive performance index as the target, and then fix the optimization results.

[0134] In some embodiments of the present invention, the following step is further included: verifying the steering control performance based on the FOPID controller.

[0135] The rudder control is handled by the inner loop of the cascade control system. The inner loop mainly controls the rudder output of the electro-hydraulic servo steering system, and its performance directly affects the heading control effect of the outer loop.

[0136] By comparing the FOPID controller with a conventional PID controller, the effectiveness of introducing an integral order into the controller can be verified. Differential order The resulting performance improvement means that FOPID has better control performance than conventional PID in terms of dynamic response, steady-state accuracy and disturbance rejection.

[0137] The HCOPSO algorithm was used to optimize the parameters of both the FOPID controller and a conventional PID controller. The number of particles in the swarm was set. The maximum number of iterations is 50. 60, particle dimension Set individual learning factors to 5 and 3 respectively. Social learning factors The values ​​are all 2. The optimization results of the inner ring rudder controller parameters are shown in Table 1.

[0138] Table 1. Results of Inner-Loop FOPID and PID Controller Parameters Optimized by HCOPSO Algorithm

[0139] Table 2. Step response index results of FOPID controller and PID inner loop steering controller

[0140] The inner loop steering step response results of the FOPID controller and the conventional PID controller are as follows: Figure 8 As shown in Table 2. From Figure 8It can be seen that both controllers can achieve stable tracking of the desired rudder angle without significant overshoot. Combining the step response indicators in Table 2, the rise time of the FOPID controller is 2.22s, slightly shorter than the 2.27s of the conventional PID controller; the settling time of the FOPID controller is 2.27s, shorter than the 2.53s of the conventional PID controller; and the steady-state error of the FOPID controller is 0.0010658°, less than the 0.0016764° of the conventional PID controller. This indicates that the FOPID controller optimized with the HCOPSO algorithm can improve the response speed and steady-state tracking accuracy of the inner-loop rudder system, allowing the rudder angle output to reach a stable state more quickly.

[0141] To further verify the anti-interference capability of the inner-loop rudder controller, an external disturbance with a positive direction and an amplitude of 4 was introduced into the rudder control system at 15 seconds into the simulation, simulating the rudder angle deflection of the unmanned surface vessel's rudder system after being subjected to a sudden load or external environmental influence. The simulation results are as follows: Figure 9 As shown. By Figure 9 It can be seen that after a disturbance is introduced, both the FOPID controller and the conventional PID controller can restore the rudder angle output to near the desired value. However, the FOPID controller exhibits a faster rudder angle deviation decay rate, a shorter steady-state recovery time, and less fluctuation during the recovery process. This result indicates that the FOPID controller has better disturbance rejection and recovery capabilities in the inner-loop rudder heading control, providing a more stable execution basis for the outer-loop heading control.

[0142] In some embodiments of the present invention, the outer-loop heading control system employs a LADRC controller, and the heading control effect of the outer-loop LADRC controller is verified. Specifically, the inner-loop steering control system is fixed to the aforementioned FOPID controller. Under the condition that the parameters of the inner-loop controller remain unchanged, an "LADRC-FOPID" cascade control system with an outer-loop LADRC controller and a "PID-FOPID" cascade control system with an outer-loop PID controller are constructed respectively, and the steering control performance and anti-interference performance of the two are compared.

[0143] The outer loop input is set to the desired heading, and the output is the actual heading. The HCOPSO algorithm is used to optimize the parameters of the LADRC controller and the PID controller respectively.

[0144] Set the number of particles The maximum number of iterations is 50. 60, particle dimension Set individual learning factors to 5 and 3 respectively. Social learning factors The values ​​are all 2. The optimization results of the outer loop controller parameters are shown in Table 3.

[0145] Table 3 Results of Outer Loop Controller Parameters Optimized by HCOPSO Algorithm

[0146] Table 4 Step Response Indicators of LADRC and PID Outer Loop Controllers

[0147] Simulation results of cascade heading control are as follows Figure 10 As shown in Table 4, when both inner loops use FOPID controllers, the overshoot of the outer loop LADRC controller is 1.9827%, which is less than the 2.6716% of the outer loop PID controller; the settling time of the outer loop LADRC controller is 2.2s, which is shorter than the 2.85s of the outer loop PID controller; and the steady-state error of the outer loop LADRC controller is 0.0089434°, which is less than the 0.012998° of the outer loop PID controller. Although the rise times of the two outer loop controllers are relatively similar, the LADRC controller performs better in terms of overshoot suppression, settling time, and steady-state accuracy. Therefore, compared to the "PID-FOPID" cascade control system, the "LADRC-FOPID" cascade control system can improve the dynamic response performance and steady-state control accuracy of the high-speed unmanned surface vessel's steering control.

[0148] When unmanned surface vessels (USVs) navigate in complex aquatic environments, they are affected by external environmental factors such as wind, ocean currents, and waves. Wind interference mainly affects the above-water portion of the hull, typically manifesting as low-frequency yaw and lateral drift. Ocean current interference primarily affects the underwater portion of the hull, altering the relative velocity between the hull and the current. Wave interference is directly caused by surface fluctuations, easily resulting in sway, pitch, and bow movements, and inducing course angle fluctuations. Compared to wind and ocean current interference, wave interference exhibits more pronounced randomness and wave frequency excitation characteristics, directly impacting the anti-interference capability of the USV's heading control system. To highlight the impact of wave disturbances on heading control performance and avoid overly complex disturbance models, this embodiment uses wave disturbances as the external environmental disturbance input.

[0149] Wave interference A second-order wave transfer function model driven by white noise is used for model establishment, namely: , in, It is white noise. For wave interference Laplace transform; The second-order wave transfer function is expressed as follows: , In the formula, These are complex frequency domain variables used for frequency domain analysis. The meanings of the remaining parameters in the formula are as follows: —Damping coefficient, with a value between 0 and 1; —Dominant wave frequency, among which The mean wave period; —Wave intensity gain, where wave energy constant , The waves are righteous and high.

[0150] Therefore, the second-order Nomoto bow roll motion model with environmental disturbances can be written as: , in, To synthesize the equivalent gain, a Laplace transform of the equation yields the actual yaw rate of the unmanned surface vessel, which originates from two sources: , in, and Yaw rate and The Laplace transform of .

[0151] Table 5 shows four typical sea state parameters as defined by the International Maritime Organization (IMO).

[0152] Table 5 Model parameters under four typical sea states

[0153] Simulation results of heading control under complex sea conditions are as follows Figure 11 As shown. Figure 11 The heading angle response curves are presented under calm sea state, microwave sea state, high sea state, and extreme sea state. The dashed line represents the reference input, the dotted line represents the response curve of the "LADRC-FOPID" cascade control system, and the solid line represents the response curve of the "PID-FOPID" cascade control system. From the overall response curves of each sub-figure, it can be seen that both cascade control methods can track the desired heading angle of 20°, indicating that the established cascade control system has basic heading maintenance capability under different sea states.

[0154] The magnified view further reveals a significant difference in the steady-state disturbance rejection performance between the two cascade control systems. In calm and microwave sea states, where wave disturbances are relatively weak, both the "LADRC-FOPID" and "PID-FOPID" cascade control systems maintain their heading angles near the desired heading. However, the heading output of the "PID-FOPID" cascade control system still exhibits some fluctuation and deviation. In contrast, the "LADRC-FOPID" cascade control system shows a smaller heading angle deviation and its response curve is closer to the reference input.

[0155] In high and extreme sea states, the intensity of wave disturbances increases, making the differences between the two cascade control systems more pronounced. The "PID-FOPID" cascade control system is significantly affected by wave disturbances, resulting in a larger deviation of the heading angle from the reference input, especially under extreme sea states. The "LADRC-FOPID" cascade control system effectively compensates for heading deviations caused by wave disturbances, keeping the actual heading angle close to the desired heading over the long term, with smaller steady-state deviations and fluctuation amplitudes.

[0156] Depend on Figure 11 The comparison results show that as the sea state gradually increases from calm to extreme, the impact of external wave disturbances on the unmanned surface vessel's heading maintenance gradually intensifies. Compared to the "PID-FOPID" cascade control system, the "LADRC-FOPID" cascade control system can simultaneously handle heading tracking and disturbance suppression, exhibiting better heading maintenance and wave interference resistance capabilities in complex sea conditions.

[0157] The simulation results above demonstrate that the FOPID controller used in this invention as the inner loop controller exhibits faster steering angle response and smaller steady-state error compared to the PID controller. Furthermore, the "LADRC-FOPID" cascade control system, with the FOPID controller as the inner loop controller and the LADRC as the outer loop controller, offers faster steering speed, higher steering accuracy, and better anti-interference performance compared to the "PID+FOPID" cascade control system, which uses the FOPID controller as the inner loop controller and the PID controller as the outer loop controller.

[0158] Finally, it should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0159] The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation of the present invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. A cascaded heading control method for an unmanned surface vessel, characterized in that... This includes the following steps: An inner-loop steering control system for an unmanned surface vessel (USV) is constructed. The inner-loop steering control system employs an FOPID controller, which outputs a control quantity to drive the USV's steering system based on the deviation between the desired steering direction and the actual steering direction. An outer-loop heading control system for an unmanned surface vessel (USV) is constructed. The outer-loop heading control system employs an ADRC controller to output the desired rudder direction of the USV based on the deviation between the desired and actual headings and the current total disturbance of the USV. The desired rudder direction of the unmanned surface vessel output by the outer ring heading control system is used as the input of the inner ring rudder control system to form a cascade control system. A step signal is input into the cascade control system to simulate the desired course of the unmanned surface vessel (USV), so that the USV can be controlled by the cascade control system.

2. The unmanned surface vessel heading cascade control method according to claim 1, characterized in that, The inner-loop steering control system includes an FOPID controller and an electro-hydraulic servo steering model. The FOPID controller is used to output control quantities for the electro-hydraulic servo steering model based on the deviation between the desired steering direction and the actual steering direction of the unmanned surface vessel. The electro-hydraulic servo steering model is used to drive the rudder steering system of the unmanned surface vessel (USV) based on the control quantity, control the rudder direction of the USV, and output the actual rudder direction of the USV.

3. The unmanned surface vessel heading cascade control method according to claim 1, characterized in that, The outer-loop heading control system includes an ADRC controller and an unmanned surface vessel maneuvering response model; The ADRC controller is used to output the desired rudder direction of the unmanned surface vessel based on the deviation between the desired and actual heading of the unmanned surface vessel. The unmanned surface vessel (USV) maneuver response model is used to output the actual heading of the USV based on its actual steering direction and the total disturbance during its movement.

4. The unmanned surface vessel heading cascade control method according to claim 3, characterized in that, The expression for the unmanned surface vessel (USV) maneuver response model is as follows: ； in, , , It is a time constant. For steering capability index, The total interference when the unmanned surface vessel is in motion. Yaw rate, Yaw angle For rudder angle.

5. The unmanned surface vessel heading cascade control method according to claim 1, characterized in that, The method for constructing the inner-loop steering control system and the outer-loop heading control system of an unmanned surface vessel specifically includes the following steps: Using a natural heuristic optimization algorithm, the parameters of the FOPID controller are optimized and tuned with a preset comprehensive performance index as the optimization target until the step response of the inner loop steering control system meets the preset performance requirements, and the optimized FOPID controller parameters are fixed. After the parameters of the FOPID controller are fixed, a natural heuristic optimization algorithm is used to optimize and tune the parameters of the ADRC controller with a preset comprehensive performance index as the optimization target until the step response of the outer loop heading control system meets the preset performance requirements, and then the optimized ADRC controller parameters are fixed.

6. The unmanned surface vessel heading cascade control method according to claim 5, characterized in that, The natural heuristic optimization algorithm is any one of the following: hybrid mean center back learning particle swarm optimization algorithm, particle swarm optimization algorithm, ant colony optimization algorithm, genetic algorithm, simulated annealing algorithm, gray wolf optimization algorithm, or whale optimization algorithm.

7. The unmanned surface vessel heading cascade control method according to claim 5, characterized in that, The comprehensive performance index is composed of a weighted combination of a time-weighted absolute error integral index, an overshoot penalty function, and a settling time penalty function; the expression for the comprehensive performance index is: ； in, For comprehensive performance indicators, The time-weighted absolute error integral index. and All are weighting factors. This is the overshoot penalty function. To adjust the time penalty function; The expression for the time-weighted absolute error integral is as follows: ； in, This represents the error between the actual output and the expected output. For time; The expression for the overshoot penalty function is: ； in, For overshoot, For the maximum overshoot, This is the actual output, and , , ; The expression for the adjustment time penalty function is: ； in, To adjust the time, Given the desired adjustment time.

8. The unmanned surface vessel heading cascade control method according to claim 6, characterized in that, When the natural heuristic optimization algorithm is a hybrid mean center inverse learning particle swarm optimization algorithm, the optimization tuning method specifically includes the following steps: The set of controller parameters to be optimized is defined as the position vector of the particle in the search space; within the preset parameter value boundary, a population consisting of multiple particles is randomly initialized, and each particle is randomly assigned an initial velocity vector; The position vector of each particle is decoded into a set of specific controller parameters and substituted into the unmanned surface vessel maneuvering response model; a preset comprehensive performance index is used as the fitness function to simulate and evaluate each particle; the current position of each particle is recorded as its individual historical best position, and the position of the particle with the best fitness is selected from all particles and recorded as the current global best position. Based on the individual's historical best position and the global best position, during the iteration process, the velocity vector and position vector of each particle in the population are updated according to the velocity and position update formula of the standard particle swarm optimization algorithm. The mean center and the partial mean center are calculated based on a predetermined calculation formula, and then the mixed mean center is calculated based on the mean center and the partial mean center. By performing reverse learning on the mixed mean center, the reverse solution of the mixed mean center is generated; Compare the fitness of the reverse solution with the current global optimum. If the reverse solution is better, then replace the current global optimum with the reverse solution. The current global optimal position is decoded into a set of controller parameters and substituted into the cascade control system; simulation is run to calculate the response data of the cascade control system under a step signal input, and the comprehensive performance index is calculated based on the response data as the optimization target value for the current iteration. When the preset algorithm termination condition is met, the iteration terminates, and the optimal controller parameter set obtained by decoding the final global optimal position is output as the optimization result.

9. A cascaded heading control system for an unmanned surface vessel, characterized in that, The method for implementing the unmanned surface vessel heading cascade control method according to any one of claims 1-8 includes: The inner loop steering control module includes an FOPID controller and an electro-hydraulic servo steering model. The FOPID controller is used to output a control quantity for the electro-hydraulic servo steering model based on the deviation between the desired steering direction and the actual steering direction of the unmanned surface vessel (USV). The electro-hydraulic servo steering model is used to drive the USV's steering system to control the USV's steering direction based on the control quantity and output the USV's actual steering direction. The outer-loop heading control module includes an ADRC controller and an unmanned surface vessel (USV) maneuvering response model. The ADRC controller outputs the desired rudder direction of the USV based on the deviation between its desired and actual heading, which serves as the input to the inner-loop rudder direction control module. The USV maneuvering response model outputs the actual heading of the USV based on its actual rudder direction and the total disturbance during its movement.

10. The unmanned surface vessel heading cascade control system according to claim 9, characterized in that, Further includes: The parameter tuning module is used to perform parameter optimization tuning operations. The parameter tuning module includes a first optimization unit and a second optimization unit. The first optimization unit is used to optimize the parameters of the FOPID controller using a natural heuristic optimization algorithm with a preset comprehensive performance index as the target, and fix the optimization result; The second optimization unit is used to optimize the parameters of the ADRC controller by using a natural heuristic optimization algorithm after fixing the parameters of the FOPID controller, with a preset comprehensive performance index as the target, and then fix the optimization result.

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